This book presents, in an integrated form, both the analysis and synthesis of three different types of hidden Markov models. Unlike other books on the subject, it is generic and does not focus on a specific theme, e.g. speech processing. Moreover, it presents the translation of hidden Markov models´ concepts from the domain of formal mathematics into computer codes using MATLAB®. The unique feature of this book is that the theoretical concepts are first presented using an intuition-based approach followed by the deillegalscription of the fundamental algorithms behind hidden Markov models using MATLAB®. This approach, by means of analysis followed by synthesis, is suitable for those who want to study the subject using a more empirical approach.
Key Selling Points:
- Presents a broad range of concepts related to Hidden Markov Models (HMM), from simple problems to advanced theory
- Covers the analysis of both continuous and discrete Markov chains
- Discusses the translation of HMM concepts from the realm of formal mathematics into computer code
- Offers many examples to supplement mathematical notation when explaining new concepts
Introduction
1. Probability theory and stochastic processes
2. Discrete hidden Markov models
3. Continuous hidden Markov models
4. Autoregressive Markov models
5. Selected Applications
Glossary
References
Index
It presents analysis of both continuous and discrete Markov chains. It deals with concepts in a generic way, most books on Hidden Markov Models focus on speech processing applications. It presents the translation of Hidden Markov Models concepts from the realm of formal mathematics into computer codes using a high-level language.
"A distinguishing feature of this book is that it provides the MATLAB code for the various algorithms covered. This would make it an excellent text for a course in the subject, as it would enable the students to experiment themselves with the algorithms encountered. Another good feature is that each chapter ends with a clear summary. All libraries serving programs in computer science should acquire this volume, and it would be worth considering as a textbook by instructors teaching courses on hidden Markov models."
— R. Bharath, emeritus, Northern Michigan University in CHOICE magazine
Inhaltsverzeichnis
Introduction
1. Probability theory and stochastic processes
2. Discrete hidden Markov models
3. Continuous hidden Markov models
4. Autoregressive Markov models
5. Selected Applications
Glossary
References
Index
